dataPlayGAME DATA COLLECTION + VISUALIZATIONDESIGNED FOR THE NON-PROFESSIONAL ATHLETELIZ RUTLEDGEINFORMATION DESIGNSPRING ...
recreational/student/youth athletes + data =                  dataPlayAN ECOSYSTEM OF STATISTICS TRACKING TOOLS THAT ARE T...
WHY:There is a growing gap between the rapidly expandingfield of data visualization and sports statistics trackingat the n...
WHAT:I seek to bridge this gap by creating an ecosystem ofeasy to use, cloud-based tools for real-time mobilestatistics co...
PURPOSE:1) to facilitate and encourage the collection of data2) to enable deeper comprehension of the datathrough visual e...
SO WHAT?This will allow for both immediate data analysis andthe preservation of spatiotemporal relationshipsbetween events...
THE PROJECT:One possible implementation of this system:an iPad app tailored to the needs of lacrosse.
HOW & WHY:Through a case study focused on several youthlacrosse teams in New York and the Bay Area,my project will demonst...
RESEARCHINFORMATION DESIGN +VISUAL THINKING
RESEARCHVISION AND COGNITION   “Cognitive Models                             “Picturing the                       of the I...
CURRENT SYSTEMTHE PAPER SCORE BOOK
current data tracking is either WAY too simple...   ...or incredibly cramped and complicated.
GROUPED IN COLUMNS BUT NO                                    HIERARCHY TO HELP LOCATE THE RIGHT STATcurrent data tracking ...
when someone scores, youneed to find their name, findthe goals column, tick off agoal, note the time on the gameclock, wri...
COMPONENT 1.0DIGITAL GAME DATA COLLECTION
all stats will be tied to game clock                             to enable time-accurate playback                         ...
NO GROUPING OFSTATS—JUST A ROW
STATISTICS GROUPED BY OFFENSEAND DEFENSE TO ALLOW MENTALCHUNKING OF ITEMS
STATISTICS GROUPED BY OFFENSE    AND DEFENSE TO ALLOW MENTAL    CHUNKING OF ITEMSBUT:NO VISUAL CUES: STILL HAVE TO READLAB...
SIMPLIFIED TO DRAG & DROP   INTERFACE USING ICONS FORIMMEDIATE VISUAL RECOGNITION
FIRST HALF    SIMPLIFIED TO DRAG & DROP    INTERFACE USING ICONS FOR IMMEDIATE VISUAL RECOGNITION   COMPLETED PASS   FORCE...
IF AN “UMBRELLA”STATISTIC GROUP ISSELECTED, REVEALSA SECOND LAYER OFINFORMATION(BUT ONLY IFRELEVANT TO YOU)
COMPONENT 2.0GAME DATA VISUALIZATION + MANIPULATION
ORIGINAL DESIGN OFVISUALIZATION INTERFACE:HIERARCHY IS WRONG!FIELD AND SCORE ARE MOSTIMPORTANT, NOT CONFUSINGLINE GRAPH OF...
CLEVELAND’S TASK MODEL #FAIL
INCREDIBLY CONFUSING!VISUALLY THERE IS NO DIFFERENTIATION      BETWEEN DIFFERENT STATISTICS                               ...
INCREDIBLY CONFUSING!VISUALLY THERE IS NO DIFFERENTIATION      BETWEEN DIFFERENT STATISTICSINTENSE COLOR ON BAR CHARTS MAK...
INCREDIBLY CONFUSING!     VISUALLY THERE IS NO DIFFERENTIATION           BETWEEN DIFFERENT STATISTICS     INTENSE COLOR ON...
THE NEWVISUALIZATION        FIELD GIVEN THEDASHBOARD         SIZE AND SPACE IT                    DESERVES FOR A          ...
THE NEWVISUALIZATION                     FIELD GIVEN THEDASHBOARD                      SIZE AND SPACE IT                  ...
THE NEWVISUALIZATION                      FIELD GIVEN THEDASHBOARD                       SIZE AND SPACE IT                ...
THANKYOU!
dataPlay: Sports Game Data Collection and Visualization [Information Design Analysis]
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dataPlay: Sports Game Data Collection and Visualization [Information Design Analysis]

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An information design analysis on some prototypes for my MFA thesis project in Design + Technology. Check out my documentation blog (lizrutledge.com/mfa-thesis) for more updates if you're interested!

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  • dataPlay: Sports Game Data Collection and Visualization [Information Design Analysis]

    1. dataPlayGAME DATA COLLECTION + VISUALIZATIONDESIGNED FOR THE NON-PROFESSIONAL ATHLETELIZ RUTLEDGEINFORMATION DESIGNSPRING 2012PARSONS MFA DESIGN + TECHNOLOGY 03/28/2012
    2. recreational/student/youth athletes + data = dataPlayAN ECOSYSTEM OF STATISTICS TRACKING TOOLS THAT ARE TAILORED TO LACROSSEBUILT ON A SYSTEM THAT COULD BE USED FOR COUNTLESS OTHER SPORTS
    3. WHY:There is a growing gap between the rapidly expandingfield of data visualization and sports statistics trackingat the non-professional level.
    4. WHAT:I seek to bridge this gap by creating an ecosystem ofeasy to use, cloud-based tools for real-time mobilestatistics collection and visualization.
    5. PURPOSE:1) to facilitate and encourage the collection of data2) to enable deeper comprehension of the datathrough visual exploration3) to enhance traditional game statistics by taggingeach piece of data with the location and time at whichit was recorded.
    6. SO WHAT?This will allow for both immediate data analysis andthe preservation of spatiotemporal relationshipsbetween events—without the steep learning curve orprohibitive costs of professional-grade tools.
    7. THE PROJECT:One possible implementation of this system:an iPad app tailored to the needs of lacrosse.
    8. HOW & WHY:Through a case study focused on several youthlacrosse teams in New York and the Bay Area,my project will demonstrate how the thoughtfulreconfiguration of existing technologies can empowera wider sports community by helping them transformdata into knowledge.
    9. RESEARCHINFORMATION DESIGN +VISUAL THINKING
    10. RESEARCHVISION AND COGNITION “Cognitive Models “Picturing the of the Influence of Concepts: An Color Scale on Data Interactive Teaching Visualization Tasks” Strategy” — Human Factors — Thinking Classroom “The Influence of Information Presentation Formats on Complex Task Decision-Making Performance” — International Journal of Human-Computer Studies “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations” — Human-Computer Interaction Laboratory and Institute for Systems Research, University of Maryland
    11. CURRENT SYSTEMTHE PAPER SCORE BOOK
    12. current data tracking is either WAY too simple... ...or incredibly cramped and complicated.
    13. GROUPED IN COLUMNS BUT NO HIERARCHY TO HELP LOCATE THE RIGHT STATcurrent data tracking is either WAY too simple... ...or incredibly cramped and complicated.
    14. when someone scores, youneed to find their name, findthe goals column, tick off agoal, note the time on the gameclock, write down the time, thenrecord the goal AGAIN down atthe bottom....UGH. space to hand-write in 25 players—most teams only have between 16- 20 players on the roster (wasted space!)
    15. COMPONENT 1.0DIGITAL GAME DATA COLLECTION
    16. all stats will be tied to game clock to enable time-accurate playback after the game is complete goals appear wherever you click the mouse to indicate field position using Javascript. Future functionality will including dragging from the goal location to indicate an assist and its location.easy counter buttonsincrement using Javascript
    17. NO GROUPING OFSTATS—JUST A ROW
    18. STATISTICS GROUPED BY OFFENSEAND DEFENSE TO ALLOW MENTALCHUNKING OF ITEMS
    19. STATISTICS GROUPED BY OFFENSE AND DEFENSE TO ALLOW MENTAL CHUNKING OF ITEMSBUT:NO VISUAL CUES: STILL HAVE TO READLABELS TO KNOW WHAT YOU’RE PRESSING
    20. SIMPLIFIED TO DRAG & DROP INTERFACE USING ICONS FORIMMEDIATE VISUAL RECOGNITION
    21. FIRST HALF SIMPLIFIED TO DRAG & DROP INTERFACE USING ICONS FOR IMMEDIATE VISUAL RECOGNITION COMPLETED PASS FORCED TURNOVER MISSED GOAL GROUND BALL FOUL MISSED PASS INTERCEPTION GOALIE SAVE ASSISTED GOAL DRAW CONTROL OUT OF BOUNDS PASSES DEFENSE SHOT ATTEMPTS POSSESSION CALLSICONS GROUPED INTO UMBRELLA GROUPSTO SIMPLIFY VISUAL LANGUAGE/AVOIDINFORMATION OVERLOAD
    22. IF AN “UMBRELLA”STATISTIC GROUP ISSELECTED, REVEALSA SECOND LAYER OFINFORMATION(BUT ONLY IFRELEVANT TO YOU)
    23. COMPONENT 2.0GAME DATA VISUALIZATION + MANIPULATION
    24. ORIGINAL DESIGN OFVISUALIZATION INTERFACE:HIERARCHY IS WRONG!FIELD AND SCORE ARE MOSTIMPORTANT, NOT CONFUSINGLINE GRAPH OF GOALSREUSEABLE ASPECT:USING VISUAL FAMILIARITYOF SCOREBOARD AND CLOCKTO MAKE INFORMATIONEASIER TO DIGEST QUICKLY
    25. CLEVELAND’S TASK MODEL #FAIL
    26. INCREDIBLY CONFUSING!VISUALLY THERE IS NO DIFFERENTIATION BETWEEN DIFFERENT STATISTICS CLEVELAND’S TASK MODEL #FAIL
    27. INCREDIBLY CONFUSING!VISUALLY THERE IS NO DIFFERENTIATION BETWEEN DIFFERENT STATISTICSINTENSE COLOR ON BAR CHARTS MAKES THEM APPEAR MOST IMPORTANT(WHEREASE THE FIELD AND CLOCK HAVE MOST IMPORTANCE) CLEVELAND’S TASK MODEL #FAIL
    28. INCREDIBLY CONFUSING! VISUALLY THERE IS NO DIFFERENTIATION BETWEEN DIFFERENT STATISTICS INTENSE COLOR ON BAR CHARTS MAKES THEM APPEAR MOST IMPORTANT (WHEREASE THE FIELD AND CLOCK HAVE MOST IMPORTANCE) BREAKING VISUAL LANGUAGE OF BARCHARTS WITH LINE CHART—NEED TO BE CONSISTENT TO TRAIN THE VIEWER CLEVELAND’S TASK MODEL UTTERLY UN-SCANNABLE #FAIL
    29. THE NEWVISUALIZATION FIELD GIVEN THEDASHBOARD SIZE AND SPACE IT DESERVES FOR A LOCATION-INCLUSIVE VISUALIZATION INTERFACE
    30. THE NEWVISUALIZATION FIELD GIVEN THEDASHBOARD SIZE AND SPACE IT DESERVES FOR A LOCATION-INCLUSIVE VISUALIZATION INTERFACE LESS SPACE DEDICATED TO BAR CHARTS BUT WITH ADDED VISUAL AIDS (ICONS)
    31. THE NEWVISUALIZATION FIELD GIVEN THEDASHBOARD SIZE AND SPACE IT DESERVES FOR A LOCATION-INCLUSIVE VISUALIZATION INTERFACE LESS SPACE DEDICATED TO BAR CHARTS BUT WITH ADDED VISUAL AIDS (ICONS) LESS SPACE USED BUT CLEARER DUE TO CONSISTER BAR CHART LANGUAGE
    32. THANKYOU!

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